Bringing innovations in the base abstractions from which developers build applications and practical implementations of those abstractions in operating systems. Our work spans from user interfaces to kernel and OS substructures. We are part of the MSR New Experiences and Technologies (NExT) organization.
We apply principles from computer science, machine learning, and statistics to genomics applications including sequence alignment, variant calling, denovo sequencing, and genome-wide association studies.
The speech group focuses on tools and algorithms that facilitate the development of innovative intelligent services using speech recognition, speech understanding and text-to-speech. The Arabic language has a special interest based on the lab’s wealth of tools for Arabic processing but we are also interested in quickly developing speech-based services in limited time and resource settings.
The NLP team at ATLC is focusing on building unsupervised pipelines for Text processing targeting a broad spectrum of tasks ranging from Input method editors to large-scale knowledge extraction.
The image understanding (IU) group aims at comprehending image and video content. Our work focuses on object and scene recognition for both category and instance levels. While we partner with key Microsoft products and services teams such as Bing and Office to transfer key technologies, our ultimate success is in impacting end users by satisfying their needs and not merely in developing technologies.
Our major goal is to build advanced deep learning technologies that empower all-seeing, all-knowing, and all-helping intelligent machines, and to work with our engineering-group partners to create the "next big things". We develop state-of-the-art technologies in knowledge management and distillation, big data analytics, internet/enterprise information processing, natural language, vision, speech, and multimodal processing. The DLTC is managed by Li Deng. We are hiring and growing!
The AI group consists of an elite team of researchers who have strong expertise in artificial intelligence, machine learning, game theory, and information retrieval. The group is devoted to the following research directions: cloud computing, robot, game-theoretic machine learning, large-scale distributed computing, and deep learning techniques for text mining.
The NExT Enable group focuses on creating technologies to help restore capabilities to people living with disabilities.
The Systems Research Group is devoted to significantly extending the state of the art in distributed systems and operating systems. Our aim is to make systems secure, scalable, fault-tolerant, manageable, and fast
The Audio and Acoustics group conducts research in audio processing and speech enhancement, 3D audio perception and technologies, devices for audio capture and rendering, array processing, information extraction from audio signals.
Our mission is to harvest and curate the wealth of knowledge encoded in language: people, content, things, connections, and activities. We mobilize research and advanced technology for the Technology arm of MSR by adapting, developing and integrating state-of-the-art technology from NLP, text mining, machine learning, knowledge extraction, and knowledge representation, while building end to end interactive knowledge experiences in close collaboration with partners across MSR and product teams.
The Knowledge Mining (KM) group at Microsoft Research Asia aims to understand and serve the world through knowledge discovery and data mining. It consists of a team of interdisciplinary researchers spanning data mining, machine learning, natural language processing, information retrieval and social computing areas.
Studio 99 is a new gallery space at Microsoft Research. Its goals are to express the creative talents of the MSR community and stimulate interesting conversations about the relationship between art and science. Many of the greatest scientists have also been artists, and the spark of creativity links both fields. By providing a space for science and art to interact, Studio 99 hopes to inspire new kinds of human expression, both scientific and artistic.
Multimedia Search and Mining (MSM) group focuses on pattern analysis and extraction for multimedia understanding, search, and data mining. We are working on research problems in search-based image annotation, large scale visual (image and video) indexing and search, sketch-based image search, object recognition with 3D structures, social multimedia analytics, etc.
Our goal is to extract biological and medical knowledge from text. Natural Language Processing tools and techniques are used in combination with biological resources.
Our mission is to explore next-generation computing systems that are scalable, efficient, robust, and easy to program.
In the System Algorithms (SysAlgo) Research Group, we focus on problems at the intersection of systems, networking, and algorithms research. We study the algorithmic foundations of the systems that drive today's computing (cloud computing, data centers, large-scale distributed systems, mobile computing, etc); and we apply our expertise in practice to advance the state of the art in applied algorithm design, and to deliver highly efficient, scalable, and robust solutions to our product groups.
In recent years, we have seen dramatic improvements in machine learning, knowledge mining, graph database, and crowdsourcing that are providing search engines with new capabilities to perform deeper data and text processing and understanding. Web Search and Data Management Group is performing cutting edge research in these related areas and developing new capabilities to empower next generation search engines and intelligent applications.
Computer Human Interactive Learning
The team of scientists in Bing that develop speech and language technologies
Researchers working on computational aspects of market design.
Web platforms such as Amazon’s Mechanical Turk are revolutionizing our ability to conduct human behavioral experiments of the kind historically performed in physical labs. Such “virtual lab” experiments allow for individual-level psychology and economics experiments to be carried out with unprecedented scale and speed, and also permit larger and more complex “networked” experiments on topics such as cooperation, learning, and collective problem solving.
With increasingly more data on every aspect of our daily activities – from what we buy, to where we travel, to who we know – we are able to measure human behavior with precision largely thought impossible just a decade ago. Lying at the intersection of computer science, statistics and the social sciences, the emerging field of computational social science uses large-scale demographic, behavioral and network data to address longstanding questions in sociology, economics, politics, and beyond.
Research of the Machine Learning group at MSR-NYC spans a wide variety of topics within theoretical and applied machine learning, including learning from interactive data (e.g., contextual bandits), large-scale machine learning, and convex optimization.